The combination of Intel SGX platform with sketching algorithms enables efficient compaction of genomic data and the execution of secure GWAS in an untrusted cloud environment.
Harnessing the power of computational science
The use and development of sophisticated computing capabilities to analyse and solve real-world, challenging problems has undoubtedly revolutionized the way researchers do science. Computational science is inherently multidisciplinary, where developed techniques can have a broad impact on the scientific community. Nature Computational Science, a new journal from the Nature Research family launching January 2021, will provide a unified home for exceptional work being done in this field. To mark the impending launch of the journal, the editors have curated a collection of content to showcase how computational science is being developed and implemented in a variety of domains.
Biology, Bioengineering, and Medicine
Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
scVelo reconstructs transient cell states and differentiation pathways from single-cell RNA-sequencing data.
Biophysical prediction of protein–peptide interactions and signaling networks using machine learning
Protein–peptide interactions that underpin cell signaling are accurately predicted by wedding the strengths of machine learning with the interpretability of biophysical theory, facilitating detailed mechanistic analyses at the proteome scale.
An advanced CellNet pipeline models the dynamics of stem cell differentiation.
Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.
The discovery of hierarchies in biological processes is central to developmental biology. Here the authors propose Poincaré maps, a method based on hyperbolic geometry to discover continuous hierarchies from pairwise similarities.
Chemistry and Materials Science
A modelling approach based on complex networks is used to simulate carrier transport in assemblies of nanostructures with a broad range of shapes and electrical properties, relevant to the realization of efficient transparent conductors.
This Perspective describes the application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process.
Quantum machine learning with improved data efficiency and transferability has been achieved using on-the-fly selection of query-dependent training molecules, which are drawn from a ‘dictionary’ of atom-in-molecule-based fragments. The benefits of the resulting models have been demonstrated for important molecular properties and for systems including organic molecules, 2D materials, water clusters, DNA base pairs and ubiquitin.
From desktop to benchtop with automated computational workflows for computer-aided design in asymmetric catalysis
Computational chemistry has remained largely inaccessible to the experimental chemistry community. Here we report the VIRTUAL CHEMIST, a software suite free for academic use, that enables organic chemists without expertise in computational chemistry to perform virtual screening experiments for asymmetric catalyst discovery and design.
First-principles-based multiscale models provide mechanistic insight and allow screening of large materials spaces to find promising new catalysts. In this Review, Reuter and co-workers discuss methodological cornerstones of existing approaches and highlight successes and ongoing developments in the field.
Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.
Photon-induced charge separation phenomena are at the heart of light-harvesting applications but challenging to be described by quantum mechanical models. Here the authors illustrate the potential of machine-learning approaches towards understanding the fundamental processes governing electronic excitations.
Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides.
Physics and Network Science
Rich data are revealing that complex dependencies between the nodes of a network may not be captured by models based on pairwise interactions. Higher-order network models go beyond these limitations, offering new perspectives for understanding complex systems.
A quantum circuit-based algorithm inspired by convolutional neural networks is shown to successfully perform quantum phase recognition and devise quantum error correcting codes when applied to arbitrary input quantum states.
As the quark–gluon plasma is a short-lived state of matter, its properties cannot be measured directly. A Bayesian parameter estimation method now provides a reliable estimation of the temperature-dependent specific shear and bulk viscosities.
A protocol for the reliable, efficient and precise characterization of quantum noise is reported and implemented in an architecture consisting of 14 superconducting qubits. Correlated noise within arbitrary sets of qubits can be easily detected.
An artificial intelligence-based method may infill gaps in historical temperature data more effectively than conventional techniques. Application of this method reveals a stronger global warming trend between 1850 and 2018 than estimated previously.
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range of grid spacings.
Victoria continental microplate dynamics controlled by the lithospheric strength distribution of the East African Rift
One of the largest continental microplates on Earth is situated in the center of the East African Rift System, and oddly, the Victoria microplate rotates counterclockwise with respect to the neighboring African tectonic plate. Here, the authors' modelling results suggest that Victoria microplate rotation is caused by edge-driven lithospheric processes related to the specific geometry of rheologically weak and strong regions.